The teaching is organized as follows:

Learning objectives

The course aims to introduce students to the statistical models used in data science. The foundations of statistical learning (supervised and unsupervised) will be developed by placing the emphasis on the mathematical basis of the different state-of-the-art methodologies. It also aims to provide rigorous derivations of the methods currently used in industrial and scientific applications to allow students to understand their requirements for correct use. Laboratory sessions will illustrate the use of fundamental algorithms and industrial case studies in which the student will be able to learn to analyze real data-sets by means of Python software. At the end of the course the students have to demonstrate the following skills: - knowledge of the main stages of data preparation, model creation and evaluation - ability to develop solutions for feature selection - knowledge and ability to use the main regression and regularization models (e.g., LASSO, Ridge Regression) - knowledge and ability to use the main methods for dimensionality reduction (e.g., Principal Component Regression, Partial Least Squares); - knowledge and ability to use the main methods for classification (e.g., KNN, Logistic Regression, LDA); - knowledge and ability to use the main methods for tree-based regression and classification (e.g., decision tree, random forest, AdaBoost) - knowledge and ability to use the main methods for unsupervised data analysis (e.g., K-means clustering, hierarchical clustering); - knowledge and ability to use the main methods for generating and training artificial neural networks (e.g., ANN for regression and classification, backpropagation and gradient descent methods, regularization for preventing overfitting, deep neural networks).